Tert-Butylhydroquinone alleviates insulin resistance and liver steatosis in diabetes.

OBJECTIVES: This work aimed to determine tert-Butylhydroquinone (TBHQ)'s effects on insulin resistance (IR) and liver steatosis in diabetic animals and to explore the underpinning mechanisms. MATERIALS AND METHODS: Male ApoE-/-mice underwent streptozocin (STZ) administration while receiving a sucrose/fat-rich diet for type 2 diabetes mellitus (T2DM) establishment. This was followed by a 6-week TBHQ administration. Body weight, fasting (FBG) and postprandial (PBG) blood glucose amounts, and insulin concentrations were measured, and the oral glucose tolerance test (OGTT) was carried out. Hematoxylin and eosin (H and E) staining and immunoblot were carried out for assessing histology and protein amounts in the liver tissue samples. In addition, cultured HepG2 cells were administered HClO and insulin for IR induction, and immunoblot was carried out for protein evaluation. Finally, the cells were stained with the Hoechst dye for apoptosis evaluation. RESULTS: The model animals showed T2DM signs, and TBHQ decreased FBG, ameliorated glucose tolerance and reduced liver steatosis in these animals. In addition, TBHQ markedly upregulated AMPKα2, GLUT4 and GSK3 ß, as well as phosphorylated PI3K and AKT in the liver of mice with T2DM. In agreement, TBHQ decreased HClO-and insulin-related IR in cells and suppressed apoptosis through AMPKα2/PI3K/AKT signaling. CONCLUSIONS: TBHQ alleviates IR and liver steatosis in a mouse model of T2DM likely through AMPKα2/PI3K/AKT signaling.

Combined model of radiomics, clinical, and imaging features for differentiating focal pneumonia-like lung cancer from pulmonary inflammatory lesions: an exploratory study.

BACKGROUND: Only few studies have focused on differentiating focal pneumonia-like lung cancer (F-PLC) from focal pulmonary inflammatory lesion (F-PIL). This exploratory study aimed to evaluate the clinical value of a combined model incorporating computed tomography (CT)-based radiomics signatures, clinical factors, and CT morphological features for distinguishing F-PLC and F-PIL. METHODS: In total, 396 patients pathologically diagnosed with F-PLC and F-PIL from two medical institutions between January 2015 and May 2021 were retrospectively analyzed. Patients from center 1 were included in the training (n = 242) and internal validation (n = 104) cohorts. Moreover, patients from center 2 were classified under the external validation cohort (n = 50). The clinical and CT morphological characteristics of both groups were compared first. And then, a clinical model incorporating clinical and CT morphological features, a radiomics model reflecting the radiomics signature of lung lesions, and a combined model were developed and validated, respectively. RESULTS: Age, gender, smoking history, respiratory symptoms, air bronchogram, necrosis, and pleural attachment differed significantly between the F-PLC and F-PIL groups (all P < 0.05). For the clinical model, age, necrosis, and pleural attachment were the most effective factors to differentiate F-PIL from F-PLC, with the area under the curves (AUCs) of 0.838, 0.819, and 0.717 in the training and internal and external validation cohorts, respectively. For the radiomics model, five radiomics features were found to be significantly related to the identification of F-PLC and F-PIL (all P < 0.001), with the AUCs of 0.804, 0.877, and 0.734 in the training and internal and external validation cohorts, respectively. For the combined model, five radiomics features, age, necrosis, and pleural attachment were independent predictors for distinguishing between F-PLC and F-PIL, with the AUCs of 0.915, 0.899, and 0.805 in the training and internal and external validation cohorts, respectively. The combined model exhibited a better performance than had the clinical and radiomics models. CONCLUSIONS: The combined model, which incorporates CT-based radiomics signatures, clinical factors, and CT morphological characteristics, is effective in differentiating F-PLC from F-PIL.

Development and validation of a novel risk assessment model to estimate the probability of pulmonary embolism in postoperative patients.

Pulmonary embolism (PE) is a leading cause of mortality in postoperative patients. Numerous PE prevention clinical practice guidelines are available but not consistently implemented. This study aimed to develop and validate a novel risk assessment model to assess the risk of PE in postoperative patients. Patients who underwent Grade IV surgery between September 2012 and January 2020 (n = 26,536) at the Affiliated Dongyang Hospital of Wenzhou Medical University were enrolled in our study. PE was confirmed by an identified filling defect in the pulmonary artery system in CT pulmonary angiography. The PE incidence was evaluated before discharge. All preoperative data containing clinical and laboratory variables were extracted for each participant. A novel risk assessment model (RAM) for PE was developed with multivariate regression analysis. The discrimination ability of the RAM was evaluated by the area under the receiver operating characteristic curve, and model calibration was assessed by the Hosmer-Lemeshow statistic. We included 53 clinical and laboratory variables in this study. Among them, 296 postoperative patients developed PE before discharge, and the incidence rate was 1.04%. The distribution of variables between the training group and the validation group was balanced. After using multivariate stepwise regression, only variable age (OR 1.070 [1.054-1.087], P < 0.001), drinking (OR 0.477 [0.304-0.749], P = 0.001), malignant tumor (OR 2.552 [1.745-3.731], P < 0.001), anticoagulant (OR 3.719 [2.281-6.062], P < 0.001), lymphocyte percentage (OR 2.773 [2.342-3.285], P < 0.001), neutrophil percentage (OR 10.703 [8.337-13.739], P < 0.001), red blood cell (OR 1.872 [1.384-2.532], P < 0.001), total bilirubin (OR 1.038 [1.012-1.064], P < 0.001), direct bilirubin (OR 0.850 [0.779-0.928], P < 0.001), prothrombin time (OR 0.768 [0.636-0.926], P < 0.001) and fibrinogen (OR 0.772 [0.651-0.915], P < 0.001) were selected and significantly associated with PE. The final model included four variables: neutrophil percentage, age, malignant tumor and lymphocyte percentage. The AUC of the model was 0.949 (95% CI 0.932-0.966). The risk prediction model still showed good calibration, with reasonable agreement between the observed and predicted PE outcomes in the validation set (AUC 0.958). The information on sensitivity, specificity and predictive values according to cutoff points of the score in the training set suggested a threshold of 0.012 as the optimal cutoff value to define high-risk individuals. We developed a new approach to select hazard factors for PE in postoperative patients. This tool provided a consistent, accurate, and effective method for risk assessment. This finding may help decision-makers weigh the risk of PE and appropriately select PE prevention strategies.

Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma.

BACKGROUND: Based on the "seed and soil" theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC). METHODS: Radiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (-) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model's performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed. RESULTS: Thirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691-0.927 in the training cohort and 0.700-0.951 in the validation cohort, respectively, thereby indicating good performance. CONCLUSION: CT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.

Hematological Indexes Can Be Used to Predict the Incidence of Hypothyroidism in Nasopharyngeal Carcinoma Patients after Radiotherapy.

BACKGROUND: This study explored the relationship between thyroid-associated antibodies, immune cells, and hypothyroidism to establish a predictive model for the incidence of hypothyroidism in patients with nasopharyngeal carcinoma (NPC) after radiotherapy. METHODS: A total of 170 patients with NPC treated at the Cancer Hospital of University of Chinese Academy of Sciences between January 2015 and August 2018 were included. The complete blood count, biochemical, coagulation function, immune cells, and thyroid-associated antibodies tested before radiotherapy were evaluated. A logistic regression model was performed to elucidate which hematological indexes were related to hypothyroidism development. A predictive model for the incidence of hypothyroidism was established. Internal verification of the multifactor model was performed using the tenfold cross-validation method. RESULTS: The univariate analysis showed that immune cells had no statistically significant differences among the patients with and without hypothyroidism. Sex, N-stage, antithyroid peroxidase antibody (TPO-Ab), antithyroglobulin antibody (TG-Ab), thyroglobulin (TG), and fibrinogen (Fb) were associated with hypothyroidism. Males and early N-stage were protective factors of thyroid function, whereas increases in TPO-Ab, TG-Ab, TG, and Fb counts were associated with an increased rate of hypothyroidism incidence. The multivariate analysis showed that TPO-Ab, TG-Ab, TG, and Fb were independent predictors of hypothyroidism. The comprehensive effect of the significant model, including TPO-Ab, TG-Ab, TG, and Fb counts, represented the optimal method of predicting the incidence of radiation-induced hypothyroidism (AUC = 0.796). Tenfold cross-validation methods were applied for internal validation. The AUCs of the training and testing sets were 0.792 and 0.798, respectively. CONCLUSION: A model combining TPO-Ab, TG-Ab, TG, and Fb can be used to screen populations at a high risk of developing hypothyroidism after radiotherapy.